Data transfer path evaluation using filtering and change detection

ABSTRACT

If a condition in a data transfer path is modeled appropriately, then a filter-based approach can be used to provide an estimate of the condition. This permits accurate, real-time estimates of the condition with modest requirements for data processing and memory resources. Change detection can be implemented to control a parameter of the filter.

CROSS REFERENCE TO RELATED APPLICATION

The subject matter disclosed in this application is related to subjectmatter disclosed in copending U.S. Ser. No. 11/285,723 (Docket No.P21435 US1).

FIELD OF THE INVENTION

The invention relates generally to data networks and, more particularly,to evaluation of data transfer paths in data networks.

BACKGROUND OF THE INVENTION

The documents listed below are incorporated herein by reference:

-   G. Bishop, and G. Welch, “An Introduction to the Kalman Filter”,    SIGGRAPH 2001, Course 8.-   K. Jacobsson, H. Hjalmarsson, N. Möller and K H Johansson,    “Estimation of RTT and bandwidth for congestion control applications    in communication networks”, in IEEE Conference on Decision and    Control (CDC) 2004 Proceedings, Paradise Island, Bahamas, 2004.-   M. Jain and C. Dovrolis, “Pathload: a measurement tool for    end-to-end available bandwidth”. In Proc. of Passive and Active    Measurement workshop (PAM), 2002.-   M. Jain and C. Dovrolis, “End-to-end Available Bandwidth:    Measurement Methodology, Dynamics, and Relation with TCP    Throughput”. In Proc. of ACM Sigcomm, 2002.-   S. Keshav, A control-theoretic approach to flow control. In    Proceedings of ACM SIGCOMM'91, pages 3-15, Zurich, Switzerland,    September 1991.-   M. Kim and B. Noble, “SANE: stable agile network estimation”,    University of Michigan Department of Electrical Engineering and    Computer Science. CSE-TR-432-00. 2000.-   M. Kim and B. Noble, “Mobile network estimation”, Mobile Computing    and Networking (ACM MOBICOM), Rome, Italy, 2001.-   B. Melander, M. Björkman, and P. Gunningberg, “A new end-to-end    probing and analysis method for estimating bandwidth bottlenecks”,    Proceedings of IEEE Globecomm'00, San Francisco, USA, November 2000.-   A. Pásztor and D. Veitch, “The packet size dependence of packet-pair    like methods”, in Proceedings, Tenth International Workshop on    Quality of Service (IWQoS 2002), Miami Beach, USA, May 2002.-   V. Ribeiro, R. Riedi, R. Baraniuk, J, Navratil, L. Cottrell,    “pathChirp: Efficient Available Bandwidth Estimation for Network    Paths”, in Proc. of Passive and Active Measurement workshop (PAM),    2003.-   A. Shriram, M. Murray, Y. Hyun, N. Brownlee, A. Broido, M. Fomenkov    and K C Claffy, “Comparison of Public End-to-End Bandwidth    Estimation Tools on High-Speed Links”, in Passive and Active    Measurement workshop (PAM), 2005.-   Nowak, R. D. and Coates, M. J., “Network tomography using closely    spaced unicast packets”, U.S. Pat. No. 6,839,754 (2005).-   M. J. Coates and R. D. Nowak. “Sequential Monte Carlo inference of    internal delays in nonstationary data networks”, IEEE Transactions    on Signal Processing, 50(2):366-376, February 2002.-   S. Ekelin and M. Nilsson, “Continuous monitoring of available    bandwidth over a network path”, 2^(nd) Swedish National Computer    Networking Workshop, Karlstad, Sweden, Nov. 23-24, 2004.-   Prasad, R., Murray, M., Dovrolis, C., Claffy, K C.: Bandwidth    Estimation: Metrics, Measurement Techniques, and Tools. In: IEEE    Network Magazine (2003).

The capability of measuring available bandwidth end-to-end over a pathin a data network is useful in several contexts, including SLA (ServiceLevel Agreement) verification, network monitoring and server selection.Passive monitoring of available bandwidth of an end-to-end network pathis possible in principle, provided all of the network nodes in the pathcan be accessed. However, this is typically not possible, and estimationof available end-to-end bandwidth is typically done by active probing ofthe network path. The available bandwidth can be estimated by injectingprobe traffic into the network, and then analyzing the observed effectsof cross traffic on the probes. This kind of active measurement requiresaccess to the sender and receiver hosts (nodes) only, and does notrequire access to any intermediate nodes in the path between the senderand receiver.

Conventional approaches to active probing require the injection of probepacket traffic into the path of interest at a rate that is sufficienttransiently to use all available bandwidth and cause congestion. If onlya small number of probe packets are used, then the induced transientcongestion can be absorbed by buffer queues in the nodes. Accordingly,no packet loss is caused, but rather only a small path delay increase ofa few packets. The desired measure of the available bandwidth isdetermined based on the delay increase. Probe packets can be sent inpairs or in trains, at various probe packet rates. The probe packet ratewhere the path delay begins increasing corresponds to the point ofcongestion, and thus is indicative of the available bandwidth. Probepackets can also be sent such that the temporal separation between probepackets within a given train varies, so each train can cover a range ofprobe rates.

Conventional solutions such as those mentioned above either do notproduce real time estimates of available bandwidth, or do not producesufficiently accurate estimates of available bandwidth, or both. Thesesolutions also tend to require either significant data processingresources, or significant memory resources, or both.

It is therefore desirable to provide for an active probing solution thatcan estimate the available bandwidth of a path in a data network withoutthe aforementioned difficulties of conventional solutions.

SUMMARY OF THE INVENTION

Exemplary embodiments of the invention provide for modeling a conditionin a data transfer path appropriately to permit the use of afilter-based approach to provide an estimate of the condition. Thispermits accurate, real-time estimates of the condition with modestrequirements for data processing and memory resources. Some embodimentsimplement change detection to control a parameter of the filter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates exemplary operations that can be performed accordingto the invention.

FIG. 2 diagrammatically illustrates an apparatus for evaluating a datatransfer path according to exemplary embodiments of the invention.

FIG. 3 graphically illustrates a piecewise linear model utilized byexemplary embodiments of the invention.

FIG. 4 diagrammatically illustrates a change detection apparatus thatutilizes filter residuals according to exemplary embodiments of theinvention.

FIG. 5 diagrammatically illustrates the alarm logic of FIG. 4 in moredetail according to exemplary embodiments of the invention thatimplement a cumulative sum test.

FIG. 6 diagrammatically illustrates further change detection techniquesthat are utilized according to exemplary embodiments of the invention.

FIG. 7 diagrammatically illustrates the alarm logic of FIG. 4 in moredetail according to exemplary embodiments of the invention thatimplement a Generalized Likelihood Ratio test.

DETAILED DESCRIPTION

Exemplary embodiments of the invention provide for fast and accurateestimation of a time-dependent condition associated with apacket-switched network path, for example, a path between two hosts ornetwork nodes on the Internet or another IP network. Active probing isused in combination with filtering to estimate variables of networkmodel related to the condition of interest. The estimate of thecondition of interest is then obtained based on the estimated variables.

The properties of the network path are sampled by transmitting probepackets in a specific pattern across the network path. Their time stampsare recorded on sending and on receiving, providing a measurement of aquantity related to the network model variables. This is repeated overand over again, for as long as it is desired to keep track of thecondition of interest.

The use of filtering enables real-time estimation. For each newmeasurement obtained by sampling the system, a new estimate of thenetwork model variable(s) is calculated from the previous estimate andthe new measurement. This in turn allows for the production of anupdated estimate (i.e., the new estimate) of the condition of interestfor each new sampling of the network path. The sampling (i.e.,measurements) may be performed arbitrarily often, resulting in real-timeresponsiveness. Thus, a filter can take a previous estimate {circumflexover (x)}_(k-1) of the system state and a new measurement z_(k) asinputs, and then calculate a new estimate {circumflex over (x)}_(k) ofthe system state based on those inputs. This permits the filter toproduce the state variable estimates in real-time, i.e. tracking thesystem state.

One example of an estimating filter is the well-established Kalmanfilter, which is both fast and lightweight. In order to be able to applya Kalman filter, both the system model and the measurement model need tobe expressed in a linear way. That is, the new system state dependslinearly on the previous system state, plus a noise term, and themeasured quantity depends linearly on system state variables, plus anoise term.

Before further discussion of the filter-based approach to the estimationof system state variables, examples of a condition to be estimated and asuitable measurement technique will now be discussed.

In some embodiments of the invention, for example, the condition to beestimated is the available bandwidth of the network path. The concept ofavailable bandwidth can be understood as follows. Each link j in anetwork path has a certain capacity, Cj, determined by the networkinterfaces in the nodes on each end of the link. The capacity Cj issimply the highest possible bit rate over the link at the packet level.The capacity typically does not vary over short time scales. However,the load, or cross traffic, on a given link j, denoted by Sj(t), doesvary. The available bandwidth Bj(t) of link j is Bj(t)=Cj−Sj(t). One oflinks j along the path has the smallest available bandwidth. This“bottleneck link” determines the available bandwidth of the path. Theavailable bandwidth B(t) of a network path is the minimum of theavailable bandwidths respectively associated with its constituent links:

${B(t)} = {\min\limits_{j}{\left( {{Cj} - {{Sj}(t)}} \right).}}$

The available bandwidth of a network path at any time t can thus beinterpreted as the maximum increase in data rate, from sending end toreceiving end, which can theoretically occur at time t without causingcongestion.

It should be noted that cross traffic load and thus available bandwidthare defined using some averaging time scale tau, i.e. Sj(t) iscalculated by averaging over some time interval of length tau aroundtime t. There is no universal, natural choice of tau, rather thisdepends on the specifics of the application. Due to finite link capacityand cross-traffic packet size, tau may not be chosen arbitrarily small.However, for modern data networks, available bandwidth and cross trafficload could be defined using tau as low as in the sub-second region.

In some embodiments of the invention, the active probing used to samplefor measurements is built on the well-known practice of transmittingpairs of time-stamped probe packets from sender to receiver. Forexample, a network path can be modeled as a series of hops, where eachhop contains an input FIFO queue and a transmission link. Eachtransmission link j has a constant capacity Cj, and time-varying crosstraffic. Consider a sequence of packets wherein the ith packet in thesequence arrives at a hop at time τ_(i), and arrives at the next hop attime τ_(i)*. Of interest are the inter-arrival times of the packetpairs. If each packet pair is defined to contain the (i−1)th packet andthe ith packet of a sequence of packets, then the inter-arrival times ofa given packet pair at the aforementioned hops are

t _(i)=τ_(i)−τ_(i-1) and t _(i)*=τ_(i)*−τ_(i-1)*.

A dimensionless quantity, the inter-packet strain, is designated as ε,and is defined by

$\frac{t_{i}^{*}}{t_{i}} = {1 + {ɛ.}}$

If u is the probe packet transmission rate selected for performing themeasurement, if r is the rate of probe packet traffic exiting a hop, andif b is the size of the probe packets, then

$\begin{matrix}{\frac{u}{r} = \frac{b/t_{i}}{b/t_{i}^{*}}} \\{= \frac{t_{i}^{*}}{t_{i}}} \\{= {1 + {ɛ.}}}\end{matrix}$

The inter-packet strain ε can thus be seen to provide an indication ofthe relationship between the probe traffic rate u and the availablebandwidth B. If u is less than B, then ε=0, and there is no congestion.However, if u reaches B, then there is congestion, and the congestion(or more exactly the strain) grows in proportion to the overload, u-B.This has been shown using a fluid model in the aforementioned document,“A new end-to-end probing and analysis method for estimating bandwidthbottlenecks”, by B. Melander et al. The above-described behavior of theinter-packet strain is demonstrated in Equation 1 below.

$\begin{matrix}{ɛ = {v + \left\{ \begin{matrix}0 & \left( {u < B} \right) \\{{\alpha \left( {u - B} \right)} = {{\alpha \; u} + \beta}} & \left( {u \geq B} \right)\end{matrix} \right.}} & (1)\end{matrix}$

In the model of Equation 1, α is a state variable equal to 1/C, β is astate variable equal to (S/C)−1, and v is measurement noise. Theavailable bandwidth is related to the state variables as

$B = {- {\frac{\beta}{\alpha}.}}$

Thus, an estimate of the available bandwidth can be readily obtained ifthe state variables α and β can be estimated.

Returning now to the idea of a filter-based approach, the state of asystem is estimated from repeated measurements of some quantitydependent on the system state, given models of how the system stateevolves from one measurement occasion to the next, and how the measuredquantity depends on the system state. Both these dependencies include arandom noise term, the process noise and the measurement noise,respectively. The system equations are then

$\begin{matrix}\left\{ \begin{matrix}{x_{k} = {{f\left( x_{k - 1} \right)} + w_{k - 1}}} \\{z_{k} = {{h\left( x_{k} \right)} + v_{k}}}\end{matrix} \right. & (2)\end{matrix}$

where x is the state of the system, z is the measurement, w is theprocess noise and v, is the measurement noise. The functions ƒ and hrepresent the system evolution model and the measurement model,respectively. The subscript refers to the “discrete time”.

If the functions ƒ and h are linear, and if both the process noise andthe measurement noise are Gaussian and uncorrelated, there is an optimalfilter, namely the Kalman filter. Experience has shown that Kalmanfilters often work very well, even when these conditions are notstrictly met. That is, the noise distributions do not need to be exactlyGaussian. A certain deviation from linearity of the functions ƒ and hcan also be accommodated by the noise terms. Another important advantagewith Kalman filters is that, unless the dimensionality of the system isvery large, they are computationally light-weight, with minimalrequirements on CPU and memory. In this linear case, the system can beexpressed using matrices as follows:

$\begin{matrix}\left\{ \begin{matrix}{x_{k} = {{Ax}_{k - 1} + w_{k - 1}}} \\{z_{k} = {{Hx}_{k} + v_{k}}}\end{matrix} \right. & (3)\end{matrix}$

The Kalman filter equations allowing calculation of the new estimatefrom the previous estimate and the new measurement are:

{circumflex over (x)} _(k) ={circumflex over (x)} _(k) +K _(k)(z _(k)−H{circumflex over (x)} _(k) )

P _(k)=(I−K _(k) H)P _(k)   (4)

where

{circumflex over (x)} _(k) =A{circumflex over (x)}_(k-1)

P _(k) =AP _(k-1) A ^(T) +Q  (5)

K _(k) =P _(k) H ^(T)(HP _(k) H ^(T) +R)⁻¹.  (6)

Kalman filtering can be understood as a process where there are twophases of calculation in each iteration. First, there is a “prediction”phase, where the previous estimate evolves one discrete time step(Equation 5) according to the system model. Then, there is a“correction” phase, where the new measurement is taken into account(Equation 4). The updated error covariance matrix P_(k) of the stateestimate is also computed.

As can be seen from Equations 6 and 5, the Kalman gain K_(k) increaseswith Q and decreases with R. These Kalman filter inputs Q and R are thecovariances of the process noise w and measurement noise v,respectively. These quantities may be intuitively understood as follows.Large variations of the noise in the system model (high Q) means thatthe prediction according to the system model is likely to be lessaccurate, and the new measurement should be more heavily weighted. Largevariations in the measurement noise (high R) means that the newmeasurement is likely to be less accurate, and the prediction should bemore heavily weighted. Note that the measured quantity z=ε is a scalar,and R is also a scalar (or a 1×1 matrix).

Exemplary embodiments of the invention make use of a Kalman filter inorder to produce an updated estimate of the available bandwidth over anetwork path for each new measurement. Each measurement includes sendinga sequence of pairs of time-stamped packets, and calculating the averageinter-packet strain. In some embodiments, the sequence of packet pairsmay be coalesced into a train. The variance of the inter-packet strainis also computed to produce the R input for the Kalman filter equations.In some embodiments, the probing rate u is chosen randomly for eachmeasurement, for example, according to a probability distribution suchas a uniform distribution. In some embodiments, measurements arerepeated after a time interval chosen randomly for each new measurementfrom a probability distribution, for example, a one-point distribution,where the interval one second is chosen with probability one.

It should be noted that the Kalman filter method is very “forgiving”,and good results are often produced even when the ideal conditions areslightly broken. So, even if a system displays characteristics thatdeviate somewhat from this piecewise linear system curve, the resultingavailable bandwidth estimate is not automatically invalidated. Ofcourse, all variables in this model are dynamical, i.e. may vary intime, so they depend on the subscript (which is sometimes suppressed inthis exposition).

The model of Equation 1 is not linear, but it is piecewise linear. Anexample of this is shown graphically in FIG. 3. Although Kalman filtersare not normally applicable in such cases, this problem is circumventedaccording to exemplary embodiments of the invention, and efficientconvergence can be obtained. Even if the piecewise linear model used isonly an approximation, the Kalman filter can still produce goodestimates, as the noise terms can accommodate some errors due to“mismodeling”. The model of Equation 1 allows for application of aKalman filter, when the state of the system is represented by a vectorcontaining the two parameters of the sloping straight line part ofEquation 1.

$\begin{matrix}{x = {\begin{bmatrix}\alpha \\\beta\end{bmatrix}.}} & (7)\end{matrix}$

In order to fulfill the linearity criterion for applicability of Kalmanfiltering, some embodiments of the invention track u with respect to B,and attempt to stay on the sloping line part of Equation 1 (see alsoFIG. 3). Since the true value of B, the available bandwidth, is notknown, exemplary embodiments of the invention use the most recentestimate {circumflex over (B)}, or some appropriate threshold value inthe vicinity of {circumflex over (B)}. If u is smaller than thisthreshold, the measurement is disregarded and the estimate of the statevector is not updated. The measurement ε_(k) of the strain at discretetime k can be written as

ε_(k) =Hx _(k) +v _(k)  (8)

where H=[u 1]. Also, the evolution of the system state can be written as

x _(k) =x _(k-1) +w _(k-1)  (9)

Thus, the Kalman filter formalism may be applied to the state vector ofEquation 7, with A=I and z=ε, where I is the Identity matrix.

The state vector x of Equation 7 is two-dimensional, so the covariance Qof the process noise w is a 2×2 matrix. This Q matrix may be used forperformance tuning, given a reference network with controlled and knowntraffic and thus known available bandwidth. When applied to thetwo-dimensional state vector of Equation 7, the Kalman equations becomecomputationally very simple, and only a few floating-point operationsare needed at each iteration. When the filter estimates the system statevariables α and β, the estimate {circumflex over (B)} of the availablebandwidth B is easily calculated from the estimates {circumflex over(α)} and {circumflex over (β)}, as

$\hat{B} = {- {\frac{\hat{\beta}}{\hat{\alpha}}.}}$

Exemplary operations according to the invention are described in thenumbered paragraphs below. These exemplary operations are alsoillustrated at 11-18 in FIG. 1.

1. The receiver makes an initial estimate {circumflex over (x)}₀ of thestate vector, and an initial estimate P₀ of the error covariance matrixcorresponding to {circumflex over (x)}₀, as shown at 11. From{circumflex over (x)}₀, an estimate {circumflex over (B)} of theavailable bandwidth is computed, as shown at 12.

2. The sender sends a sequence (or train) of N probe packet pairs withprobe traffic intensity u, according to a desired probabilitydistribution, as shown at 13. (In some embodiments, the probabilitydistribution for u is chosen based on past experience orexperimentation.) The N probe packet pairs are used for inter-packetstrain measurement. Some embodiments send a sequence of N+1 probepackets that are used to form N pairs of probe packets. That is, thesecond probe packet of the first pair is also used as the first probepacket of the second pair, and so on. In some embodiments, probe packetsare not shared among the probe packet pairs, so a sequence of 2N probepackets is used to form N distinct pairs of probe packets. The senderpasses the current value of the probe packet traffic rate u to thereceiver in the probe packets.

3. For the received sequence of probe packets, the receiver recovers thetraffic intensity u from the probe packets, as shown at 14. If u exceedsa threshold value (for example, {circumflex over (B)} or a valuesuitably chosen in the vicinity of {circumflex over (B)}) at 15, thenthe receiver uses the time stamps of the probe packets to compute theaverage ε of the N inter-packet strain values corresponding to the Npairs of packets, as shown at 16. The receiver also computes thecovariance R. If u is less than or equal to the threshold value at 15,then the time-stamps of the probe packets are ignored, no update isperformed, and operations return to #2 above, as shown at 18.

4. The Kalman filter uses the average inter-packet strain value andcovariance matrix (if any) from operation 3 above to update theestimates of the state vector {circumflex over (x)} and the errorcovariance matrix P, as shown at 17. From the new state vector estimate,a new estimate {circumflex over (B)} of the available bandwidth iscomputed, as shown at 12.

5. Operations return to #2 above, and the next sequence of probe packetsis generated, as shown at 13.

FIG. 2 diagrammatically illustrates an apparatus for evaluating a datatransfer path according to exemplary embodiments of the invention. Insome embodiments, the apparatus of FIG. 2 is capable of performingoperations described above and illustrated in FIG. 1. In someembodiments, the nodes 21 and 23, and the data transfer path 22, canconstitute, or form a part of, a packet-switched data network, such asthe Internet, a private intranet, etc. The sending node 21 sendssequences of probe packet pairs through the data transfer path 22 to thereceiving node 23. The aforementioned time stamping at the sending andreceiving nodes is not explicitly shown. The receiving node 23 includesan extraction unit 24 that extracts the time stamp information and theprobe packet rate u from the probe packets. A strain calculation unit 25calculates the inter-packet strain value of each pair of probe packetsin the received sequence, and also calculates the average and varianceof the inter-packet strain values.

The strain calculation unit 25 provides the average inter-packet strainε as the z input to a Kalman filter 26, and also provides theinter-packet strain variance R as an input to the Kalman filter 26. TheKalman filter 26 also receives as inputs the Q matrix, and the initialestimates of the state vector {circumflex over (x)}₀ and the errorcovariance matrix P₀. In some embodiments, the initial estimates of thestate vector {circumflex over (x)}₀ and the error covariance matrix P₀,are derived from past experience or experimentation. The accuracy ofthese initial estimates is not a significant factor in the operation ofthe Kalman filter.

The Kalman filter 26 receives the identity matrix I as its A matrixinput, and also receives the matrix H (e.g., H=[u 1]) from a matrixgenerator 27 that produces matrix H in response to the probe packet rateu as extracted from the probe packets by extraction unit 24. In responseto its inputs, the Kalman filter 26 produces the updated state vectorestimate {circumflex over (x)}_(k). An available bandwidth calculationunit 28 uses the updated state vector estimate to update the availablebandwidth estimate {circumflex over (B)}̂. The available bandwidthestimate {circumflex over (B)}̂ is provided as an output result from thenode 23, and is also provided to a compare unit 29 that compares theprobe packet rate u to a threshold value that is equal to or suitablynear {circumflex over (B)}̂. Depending on the result of this comparison,the compare unit 29 provides to the strain calculation unit 25 anindication 20 of whether or not the strain calculation unit 25 shouldmake its calculations for the current sequence of probe packet pairs. Insome embodiments, the indication 20 is provided to the Kalman filter 26,to signal whether or not the filter should be applied to the result ofthe strain calculation unit 25. This is shown by broken line 201.

From the foregoing description of FIG. 2, the extraction unit 24 and thestrain calculation unit 25 can be seen as components of a dataproduction unit that ultimately produces estimation data (ε and R in theexample of FIG. 2) for use in estimating the available bandwidth. Also,the Kalman filter 26 and the available bandwidth estimator 28 can beseen as components of an estimation unit that applies Kalman filteringto the estimation data in order to produce the estimate of the availablebandwidth.

Some embodiments are tunable to optimize the tracking performance at adesired available bandwidth-averaging time scale tau. For example, iftau=10 seconds, then the available bandwidth measurements are made overa 10-second time scale. In some embodiments, tau=10 seconds, and the 2×2matrix Q has values of Q11=10⁻⁵, Q12=0, Q21=0, and Q22=10⁻³. Inembodiments with shorter time scales, the value of Q22 may be increased,while maintaining Q11, Q12 and Q21 the same.

Some embodiments use scaling in the numerical calculations, so that thequantities involved are dimensionless. As mentioned above, ε isdimensionless to start with, and thus β, v and R are also dimensionless.In order to make all other quantities dimensionless, the probe trafficrate u is scaled by the maximum capacity of the first hop from the probesender host/node. This means that u=1 is the maximum possible probingrate, and that α, x, w and Q are dimensionless.

In some embodiments, the components at 25-29 of FIG. 2 are providedexternally of and separate from the receiving node 23, but are coupledto the extraction unit 24 to receive therefrom the time stampinformation and the probe packet rate u. This type of partitioning isillustrated by broken line 200 in FIG. 2. In this partitioning example,all of the components 25-29 are provided externally of and separate fromthe receiving node 23, for example, in one or more other nodes of thenetwork.

The present invention can produce an updated estimate of the availablebandwidth for each new sampling of the system, i.e. for each new trainof probe packet pairs. The trains may be sent arbitrarily often (i.e.,the time scale for the sampling measurement can be reduced arbitrarily),so the available bandwidth may be tracked in real time.

Data processing and memory requirements for performing theabove-described filtering calculations can be met relatively easily. Forexample, in some embodiments the receiving node 23 of FIG. 2 is amicroprocessor platform. The update of the available bandwidth estimateusing the Kalman filter equations reduces to performing only a fewfloating-point operations (closer to 10 than to 100 in someembodiments).

In general, the use of the filter 26 of FIG. 2 provides a number ofpossibilities for tuning the filter parameters in order to achievespecific properties. Various characteristics of the filter estimates canbe obtained by choosing various filter parameters. Typically, there isnot one given set of filter parameters that is preferable in allpossible cases. The desired behavior of the filter may vary, andtherefore, so does the desirable setting of the filter parameters. Forexample, in some embodiments, the filter is designed solely for highagility, which is desirable if fast adaptation/tracking with respect tosystem changes is important. However, this also makes the filterrelatively more sensitive to measurement noise. In some embodiments, thefilter is designed for stability, so that the filter is relativelyinsensitive to measurement noise. But this makes the filter relativelyslower in adapting to abrupt changes in the system state. In someembodiments, however, the filter performs sufficiently in a variety ofconditions, exhibiting, for example, both a relatively low sensitivityto measurement noise, and relatively agile performance in adapting tochanging conditions in the system.

As described above, the available bandwidth over a packet-switchednetwork path typically varies with time. If the filter 26 of FIG. 2 isconfigured to produce high quality estimates of the available bandwidthin a network with slowly varying characteristics, then it may berelatively slow in adapting to abrupt changes in the system state. Inorder for the filter 26 to be appropriately responsive to rapidvariations in the packet-switched network environment (which couldhappen, for example, due to sudden changes in cross traffic intensityand/or alternations of the bottleneck link capacity), it is desirable toprovide the filter with an alternative set of filter parameters that areappropriate for fast adaptation. This is achieved in some embodimentsthrough the use of change detection techniques.

Change detectors can improve the performance of filter-based tools thatexperience abrupt changes in system state. A change detector canautomatically discover when the predictions of the filter face asystematic deviation from the input measurements. Based on thisinformation, the change detector can indicate when the filter seems tobe out of track. The change detector can provide the filter with asuitable alarm indication in case of poor performance due, for example,to an abrupt change in the system state. Thus, in case of an alarm,appropriate filter parameters can be temporarily adjusted from a firstset of parameters that provide stability and noise insensitivity to asecond set of parameters that achieve fast adaptation to the new systemstate. Thereafter, use of the first set of filter parameters can beresumed. Thus, there is, in general, a trade-off between the stabilityand the agility of the estimates; in other words, for example, acompromise between noise attenuation and tracking ability.

In embodiments that use change detection, rapid changes in the availablebandwidth can be accommodated while still maintaining stability. Ifabrupt changes occur in the system due, for example, to suddenvariations in cross traffic intensity and/or bottleneck link capacity,the change detector can automatically advise the filter to temporarilyapply filter parameter tuning such that the properties of a quicklyadaptive filter are obtained. In some embodiments, when there is noalarm from the change detector, the filter maintains its preconfiguredfilter parameters, which yield estimates suitable for a network withslowly changing characteristics; i.e. stability takes precedence overagility. In other embodiments, the reverse situation is implemented.That is, the filter is preconfigured for agility, and is selectivelyswitched to achieve noise insensitivity temporarily, i.e., agility takesprecedence over stability.

Some common change detection techniques take advantage of filterresiduals. A filter residual can be interpreted as a measure of thefilter's ability to predict the system state ahead of time. For example,in some embodiments, the filter residual at time k is calculated as thedifference between the measurement taken at time k, and the filter'sprediction (estimate) of that measurement. Other embodiments use thefilter residual to produce a distance measure that is related to thefilter residual, for example, producing the distance measure as asuitably normalized version of the filter residual.

FIG. 4 diagrammatically illustrates change detection logic that utilizesfilter residuals according to exemplary embodiments of the invention. Acombiner 40 combines the measurement for time k, namely z_(k), and thefilter's prediction (estimate) of the system state, namely {circumflexover (x)} _(k) (see also Equations 4 and 5), thereby producingcombination information at 45. In some embodiments, the combinationinformation includes a sequence of filter residuals at 45. In otherembodiments, the combination information at 45 includes a sequence ofdistance measures related to the filter residuals. The combinationinformation 45 is input to alarm logic 42. The alarm logic 42 performsstatistical analysis with respect to the combination information 45.Based on this analysis, the alarm logic 42 decides whether or not toactivate an alarm signal 46.

The alarm signal 46 controls a selector 43 that inputs filterparameters, notably the process noise covariance matrix Q to the Kalmanfilter 26 (see also FIG. 2). When the alarm signal 46 is active, theselector 43 passes matrix Q2 to the filter 26. Otherwise, the selectorpasses matrix Q1 to the filter 26. The matrix Q1 is designed to realizea filter that is generally characterized by stability and noiseinsensitivity, and the matrix Q2 is designed to realize a filter that isgenerally characterized by rapid adaptability to a new system state. Insome embodiments, the diagonal elements (at row 1, column 1 and row 2,column 2) of Q1 are 10⁻⁵, and the other elements of Q1 are 0. In variousembodiments, the diagonal elements (at row 1, column 1 and row 2, column2) of Q2 take values of 10⁻² or 10 ⁻¹, and the other elements of Q2 are0. In some embodiments, the measurements z_(k) are made once per second,i.e., the discrete time interval indexed by k is one second. In variousones of the aforementioned embodiments wherein the measurement intervalis one second, the alarm signal 46, once activated by alarm logic 42,remains active for a period of time that ranges from one to five of themeasurement intervals, and is then inactivated by alarm logic 42.

Some embodiments exploit the fact that a sequence 45 of filter residuals(or corresponding distance measures) can generally be expected toresemble white noise in the absence of an abrupt change in the systemstate. If an abrupt change occurs, one or more statisticalcharacteristics (e.g., the mean and/or variance) associated with thesequence of residuals (or corresponding distance measures) will changein correspondingly abrupt fashion, and this change in the statisticalcharacteristic(s) can be detected by the alarm logic 42. This iscommonly referred to as a whiteness test, because the alarm logic 42detects deviations from the white noise characteristic that the sequence45 is expected to exhibit.

As mentioned above, some embodiments use a distance measure that iscomputed as a suitably normalized representation of the correspondingfilter residual at time k, for example:

$\begin{matrix}{s_{k} = \frac{z_{k} - {H_{k}{\hat{x}}_{k}^{-}}}{\sqrt{{H_{k}P_{k}^{-}H_{k}^{T}} + R_{k}}}} & (10)\end{matrix}$

The numerator of the exemplary distance measure s_(k) represents thefilter residual at time k. The denominator normalizes the filterresidual to unit variance. The expression under the radical sign in thedenominator is the same as the expression in parentheses in Equation 6above (the equation that defines the Kalman gain K_(k)). In someembodiments, the distance measure s_(k) is used to build up thefollowing test statistic for time k:

g _(k) ^(pos)=max(g _(k-1) ^(pos) +s _(k) −v,0)

alarm if g_(k) ^(pos)>h  (11)

This test statistic, commonly referred to as a cumulative sum (CUSUM)test statistic, is an example of a statistic that can be used toimplement a whiteness test. The CUSUM test statistic detects an upwardsystematic trend of the filter residual z−Hx. The test statistic isinitialized at g₀=0. In Equation 11, v is used to designate a designparameter known as the drift parameter. If the distance measure S_(k),is smaller than the drift v, then the distance measure will notcontribute to the test statistic. The chosen value of v is an indicatorfor what can be considered as normal/random deviation between themeasurement and the prediction. After each iteration, i.e., after eachdiscrete time step, a stopping rule is applied. The stopping rule, shownabove immediately below Equation 11, compares the test statistic to athreshold h. If the test statistic exceeds the threshold h, then analarm is issued.

Because the distance measure s_(k) can be either positive or negative,some embodiments use a two-sided CUSUM test. The two-sided test uses thenegative test statistic defined below by Equation 12, together with theassociated stopping rule shown immediately below Equation 12, incombination with the positive test statistic and stopping ruleassociated with Equation 11, so that both upward and downward systematictrends of the filter residual can be tracked:

g _(k) ^(neg)=min(g _(k-1) ^(neg) +s _(k) +v,0)

alarm if g_(k) ^(neg)<−h  (12)

The above-described design parameters h and v affect the performance ofthe CUSUM test in terms of false alarm rate (FAR) and mean time todetection (MTD). Various embodiments use various design parametervalues, depending on what requirements are to be fulfilled. For example,in various embodiments, the drift parameter v is tales values in a rangefrom approximately 10⁻⁴ to approximately 10 (depending on the chosenthreshold value h and other aspects such as the desired FAR and MTD). Invarious embodiments, the threshold parameter h takes values in a rangefrom approximately 10⁻³ to approximately 40 (depending on the chosendrift value v and other aspects such as the desired FAR and MTD). Someembodiments use the following design parameter values: drift v=0.5; andthreshold h=10. The stopping rule signals an alarm if the test statisticg_(k) exceeds the specified threshold h (or −h). In various embodiments,the positive test statistic and the negative test statistic usedifferent values for their respective drift parameters and/or for theirrespective threshold parameters.

FIG. 5 diagrammatically illustrates in more detail the alarm logic 42 ofFIG. 4 according to exemplary embodiments of the invention thatimplement the cumulative sum test described above. At 45, a sequence ofthe aforementioned distance measures s_(k) is received from the combiner40 (see also FIG. 4), and is passed to positive logic 53 and negativelogic 54. The positive logic 53 computes the positive test statistic ofEquation 10, and implements the corresponding positive threshold test.The negative logic 54 computes the negative test statistic of Equation11 and implements the corresponding negative threshold test. If eitherthe positive logic 53 or the negative logic 54 triggers an alarm, at 55or 56, respectively, then the alarm signal 46 (see also FIG. 4) isactivated by OR logic 57. The positive logic 53 and negative logic 54operate concurrently to detect any new trend (e.g., any consistentdeviation from expectance value 0).

Various change detectors are implemented in various embodiments of theinvention. Examples of such embodiments are diagrammatically illustratedin FIG. 6. The broken lines in FIG. 6 illustrate that embodiments usevarious types of change detectors in conjunction with the filter 26 (seealso FIG. 2). FIG. 6 illustrates various embodiments that implementvarious change detection techniques, such as ratios and marginalizationof likelihoods 61, sliding windows 62, filter banks 63, multiple models64, and algebraical consistency tests 65.

A number of the change detectors shown in FIG. 6 can also be included inthe family of statistical change detection techniques that usehypothesis testing. One of these techniques is the GeneralizedLikelihood Ratio (GLR) test. The GLR test is well known in the art ofchange detection, as described, for example, in the following documents,which are incorporated herein by reference: A. S. Willsky and H. L.Jones, “A generalized likelihood ratio approach to the detection andestimation of jumps in linear systems”, IEEE Transactions on AutomaticControl, pages 108-112 (1976); and E. Y. Chow, “Analytical Studies ofthe Generalized Likelihood Ratio Technique for Failure Detection”, MITReport ESL-R-645 (1976). The GLR test employs parallel filters anddelivers an estimate of the time and magnitude of the change. The GLRtest makes use of a likelihood-ratio measure as the test statistic,which is used for deciding whether or not a change has occurred. Thelikelihood ratio has, for certain applications, been shown to be theoptimal test statistic when testing a hypothesis H₀ that no change hasoccurred against a hypothesis H₁(θ, v) that a change of known magnitudev occurred at a change time. In this case, H₀ and H₁ are simplehypotheses and the Likelihood Ratio (LR) test is used to decide betweenthem.

The GLR test is an extension of the LR test to include compositehypotheses (where the simple hypothesis H₀ is tested against severalalternate hypotheses, i.e. H₁ is a composite hypothesis, one for eachchange time θ). The GLR test basically compares the highest likelihoodsthat are obtainable under the respective hypotheses. This can be seen asestimating a parameter using a maximum likelihood estimator under thepair-wise comparison of different H₁ and the hypothesis H₀, and usingthe estimated parameter to construct and apply a regular likelihoodtest. The hypotheses are:

H₀: no change

H₁(θ,v): a change of magnitude v at time θ

where v and θ are unknown (to be inferred by the method).

For clarity and convenience of exposition, the following description ofthe GLR test uses the notational convention of placing the argumentsassociated with the various signals in parentheses, rather thancontinuing with the subscript notation used in Equations 1-10 above. TheGLR test makes use of a maximization over both v and θ, and the obtainedestimates are used in a regular LR test, where the log likelihood ratiois compared to a chosen decision threshold. Thus, the GLR test decidesthat a change has occurred if any of a plurality of calculated values ofa test statistic exceeds a threshold value. The threshold value in theGLR test determines how much more likely the alternative hypothesis H₁must be before it is chosen over the null hypothesis H₀.

The GLR test statistic can be defined as:

$\begin{matrix}{l = {2\; \log \; \frac{p\left( {\left( {{\gamma (1)},\ldots \mspace{14mu},{\gamma (k)}} \right)H_{1}} \right)}{p\left( \left( {{\gamma (1)},\ldots \mspace{14mu},{\gamma (k)}} \right) \middle| H_{0} \right)}}} & (13)\end{matrix}$

where γ(1), . . . , γ(k) are the filter residuals at discrete times 1 .. . k, respectively. These filter residuals are the same as defined bythe numerator in Equation 10 above.The test statistic l is a function of the latest time underconsideration, k (which is known), as well as the change magnitude v andchange time θ (both of which are unknown). In Equation 13, thelikelihoods for the observed sequence of filter residuals (γ(1), . . . ,γ(k)) at times 1 . . . k, under the hypotheses H₀ and H₁, respectively,are represented as conditional probability densities.

In some embodiments, the test statistic l is calculated as:

l(k, θ) = d^(T)(k, θ) ⋅ C⁻¹(k, θ) ⋅ d(k, θ) where${d\left( {k,\theta} \right)} = {\sum\limits_{j = \theta}^{k}{{G^{T}\left( {j,\theta} \right)} \cdot {V^{- 1}(j)} \cdot {{\gamma (j)}.}}}$

With respect to the matrix V⁻¹ in d(k,θ), note that the correspondingmatrix V is defined by the expression in parentheses in Equation 6 above(the equation for the Kalman gain), and is available from the filter 26.With respect to the matrix G^(T) in d(k,θ), the corresponding matrix Grepresents the effect on the filter residual at time j given a suddenchange v in the system state at time θ, and can be defined as follows:

G(j,θ)=0 for j<θ

G(j,θ)=H(j)·[A(j,θ)−A(j,j−1)·F(j−1, θ)] for j≧θ

From the foregoing filter design discussion, recall that A=I at alldiscrete times j. Also with respect to G(j,θ), F can be defined asfollows:

F(n, θ) = 0  for  n < θ${F\left( {n,\theta} \right)} = {{\sum\limits_{i = \theta}^{n}{{{\rho \left( {n,i} \right)} \cdot {K(i)} \cdot {H(i)} \cdot {A\left( {i,\theta} \right)}}\mspace{14mu} {for}\mspace{14mu} n}} \geq {\theta.}}$

In F(n,θ), K corresponds to the Kalman gain defined above in Equation 6.Also in F(n,θ), H can be defined as H(n)=[u(n) 1], where u(n) is theprobe traffic rate at time n, as described hereinabove, so H(n) isavailable from the filter 26. Further in F(n,θ), ρ(n,i) can be definedas

ρ(n,i)=ρ(n−1)·ρ(n−2)·K·ρ(i),

where, e.g., ρ(n−1)=[I−K(n)·H(n)]·A(n−1),or more explicitly, ρ(m)=[I−K(m+1)·H(m+1)]·A(m).Thus, the components of ρ as contained in F(n,θ) are known or availablefrom the filter 26. Therefore, all information needed to compute F(n,θ),and thus G(j,θ), is available from the filter 26. Recalling that V isavailable from the filter 26, all information needed to compute d(k,θ)is available from the filter 26.

The remaining component of l(k,θ),the matrix C⁻¹(k,θ), is the errorcovariance matrix of the maximum likelihood estimate {circumflex over(v)}, which describes the change at time {circumflex over (θ)}. It isthe inverse of the change information matrix:

${C\left( {k,\theta} \right)} = {\sum\limits_{j = \theta}^{k}{{G^{T}\left( {j,\theta} \right)} \cdot {V^{- 1}(j)} \cdot {G\left( {j,\theta} \right)}}}$

Accordingly, all information needed to calculate C(k,θ) is availablefrom the filter 26. Thus, inasmuch as d(k,θ) and C(k,θ) can be computedfrom information that is known and/or available from the filter 26,l(k,θ) can be computed from known and/or available information.

In some embodiments, at each discrete time k, a test statistic l(k,θ) iscalculated for each value of θ within a range of interest. In someembodiments, the range of interest for θ is 1≦θ≦k. More generally, invarious embodiments, the range of interest for θ is k−M≦θ≦k−N, where Mand N define a desired window of time for the change detection analysis.It can be seen that the aforementioned range 1≦θ≦k corresponds to M=k−1and N=0. The test statistic having the maximum value in the calculatedset is chosen, and is compared to a threshold value. If the maximumvalue exceeds the threshold value, then an alarm is issued. As describedabove with respect to the CUSUM threshold, the GLR threshold can be seenas a trade-off between FAR and MTD. In some embodiments, therefore, thethreshold is selected based on aspects such as the desired FAR and MTD.As examples, threshold values such as 5, 10, 15, etc., are used invarious embodiments.

The most likely change time is given by

${{\hat{\theta}(k)} = {\arg \mspace{11mu} {\max\limits_{1 \leq \theta \leq k}{l\left( {k,\theta} \right)}}}},$

and the most likely change at time {circumflex over (θ)}(k) is given by

{circumflex over (v)}=C ⁻¹(k,{circumflex over (θ)}(k))·d(k,{circumflexover (θ)}(k)).

It can be shown that the test statistic l(k,θ,v) under the nullhypothesis exhibits a x² distribution. Therefore, given a desiredconfidence level, a suitable threshold can in principle be estimatedusing statistical tables.

FIG. 7 diagrammatically illustrates in more detail the alarm logic 42 ofFIG. 4 according to exemplary embodiments of the invention thatimplement the GLR test described above. In FIG. 7, the alarm logic 42includes filter logic 71, for example, a plurality of parallel filtersas indicated. For a given discrete time k, each of the parallel filtersproduces the test statistic l(k,θ) for a respectively correspondingvalue of θ within the range of interest for θ. Thus, a total of M−N+1parallel filters are used to implement a range of k−M≦θ≦k−N. Each of thefilters receives the residuals 45 from the combiner 40 (see also FIG.4), and further receives from the filter 26 the remaining information(described in detail above and designated generally at 70 in FIG. 7)needed to compute the associated test statistic l(k,θ). The M−N+1filters at 71 output M−N+1 test statistics l(k,θ), designated generallyat 72. At 73, maximum select logic selects the maximum value from amongthe M−N+1 test statistics l(k,θ), and outputs this maximum value teststatistic at 74. Threshold compare logic 75 compares the maximum valuetest statistic 74 to a threshold value. If the maximum value teststatistic 74 exceeds the threshold value, then the threshold comparelogic 75 activates the alarm signal 46 (see also FIG. 4).

The Marginalized Likelihood Ratio (MLR) test is a change detectiontechnique that is related to the GLR technique and is applicable ingenerally the same situations where GLR is applicable. In the MLR test,the change magnitude is considered as a stochastic nuisance parameterthat is eliminated by marginalization, instead of estimation as in theGLR test.

In some embodiments, the change detection apparatus (e.g., any of thoseshown in FIGS. 4-7) is implemented in the same network node as is thefilter 26 of FIG. 2. In other embodiments, the change detectionapparatus is implemented in a network node (or nodes) other than thenetwork node (or nodes) in which the filter 26 is implemented, and allof the implicated nodes are coupled appropriately to support thecommunications shown in and described with respect to FIGS. 4-7.

Workers in the art will note that the above-described embodiments of theinvention are applicable in many areas, two examples of which arenetwork monitoring and adaptive applications. For example, networkoperators can use the invention to reliably monitor the state of theirnetworks. As a practical example, if a transmission link goes down orthe traffic load suddenly changes, it is desirable for operators toreceive a fast and evident indication of the event, so that suitableaction can be taken. As an example in the area of adaptive applications,audio/video applications in end hosts can use filter-based bandwidthestimation in order to dynamically adapt the codec to current end-to-endbandwidth availability. The performance can be enhanced by theabove-described capability of rapidly adapting to sudden changes in thenetwork.

It will be evident to workers in the art that, in various embodiments,the invention can be implemented in hardware, software, or a combinationof hardware and software.

Although exemplary embodiments of the invention have been describedabove in detail, this does not limit the scope of the invention, whichcan be practiced in a variety of embodiments.

1. An apparatus for evaluating available bandwidth in a data transferpath that transfers data between data communication nodes of a datanetwork, comprising: a filter for coupling to the data transfer path,said filter configured to use a filter operation during real-timeoperation of the data transfer path in which received data is processedat a rate at which it is received to produce real-time information foruse in estimating the available bandwidth; logic coupled to said filterfor analyzing said real-time information to determine an estimate of theavailable bandwidth; and an adjuster coupled to said logic and saidfilter for selectively adjusting input parameters to the filter inresponse to said logic, said input parameters controlling whether anestimate is produced which reflects slowly changing conditions in thenetwork or rapidly changing conditions in the network.
 2. The apparatusof claim 1, wherein said real-time information includes real-timeparameter estimates for use in estimating the available bandwidth, andwherein said filter operation uses real-time measurements which arerelated to the available bandwidth and which respectively correspond tosaid real-time parameter estimates, said logic including a combiner forcombining said real-time measurements with the respectivelycorresponding real-time parameter estimates to produce combinationinformation, and said logic configured to statistically analyze saidcombination information.
 3. The apparatus of claim 2, wherein saidcombination information is indicative of respective comparisons of saidreal-time measurements to the respectively corresponding real-timeparameter estimates.
 4. The apparatus of claim 3, wherein said logic isconfigured to compute a test statistic based on said combinationinformation, and to compare said test statistic to a threshold.
 5. Theapparatus of claim 4, wherein said logic is configured to analyze arelationship between first and second likelihoods, wherein said firstlikelihood is a likelihood that said combination information isindicative of a first real-time characteristic of the availablebandwidth, and wherein said second likelihood is a likelihood that saidcombination information is indicative of a second real-timecharacteristic of the available bandwidth.
 6. The apparatus of claim 4,wherein said logic is configured to detect whether a change in astatistical characteristic associated with said combination informationhas occurred.
 7. The apparatus of claim 3, wherein said logic isconfigured to compute a plurality of test statistics based on saidcombination information, select one of said test statistics having amaximum value among said test statistics, and compare said one teststatistic to said threshold.
 8. The apparatus of claim 2, wherein saidlogic is configured to apply a whiteness test with respect to saidcombination information.
 9. The apparatus of claim 2, wherein said logicis configured to apply a cumulative sum test with respect to saidcombination information.
 10. The apparatus of claim 2, wherein saidlogic is configured to apply a Generalized Likelihood Ratio test withrespect to said combination information.
 11. The apparatus of claim 2,wherein said logic includes a plurality parallel filters.
 12. Theapparatus of claim 1, wherein said adjuster is configured to adjust saidfilter operation to temporarily increase responsiveness thereof tochanges in the available bandwidth.
 13. The apparatus of claim 1,wherein said filter includes a Kalman filter, and said adjuster isconfigured to adjust a process noise covariance parameter provided tosaid Kalman filter.
 14. The apparatus of claim 1, wherein said logic isprovided physically separately from said filter at a location physicallyremote from said filter.
 15. A method of evaluating available bandwidthin a data transfer path that transfers data between data communicationnodes of a data network, comprising: during real-time operation of thedata transfer path, using a filter operation to produce real-timeinformation for use in estimating the available bandwidth at a rate atwhich input data is received; analyzing said real-time information todetermine an estimate of the available bandwidth; and selectivelyadjusting input parameters to the filter operation in response to saidanalyzing, said input parameters controlling whether an estimate isproduced which reflects slowly changing conditions in the network orrapidly changing conditions in the network.
 16. The method of claim 15,wherein said real-time information includes real-time parameterestimates for use in estimating the available bandwidth, and whereinsaid filter operation uses real-time measurements which are related tothe available bandwidth and which respectively correspond to saidreal-time parameter estimates, said analyzing including combining saidreal-time measurements with the respectively corresponding real-timeparameter estimates to produce combination information, and saidanalyzing including statistically analyzing said combinationinformation.
 17. The method of claim 16, wherein said combinationinformation is indicative of respective comparisons of said real-timemeasurements to the respectively corresponding real-time parameterestimates.
 18. The method of claim 17, wherein said analyzing includescomputing a test statistic based on said combination information, andcomparing said test statistic to a threshold.
 19. The method of claim18, wherein said analyzing includes analyzing a relationship betweenfirst and second likelihoods, wherein said first likelihood is alikelihood that said combination information is indicative of a firstreal-time characteristic of the available bandwidth, and wherein saidsecond likelihood is a likelihood that said combination information isindicative of a second real-time characteristic of the availablebandwidth.
 20. The method of claim 18, wherein said analyzing includesdetecting whether a change in a statistical characteristic associatedwith said combination information has occurred.
 21. The method of claim16, wherein said analyzing includes applying a cumulative sum test withrespect to said combination information.
 22. The method of claim 16,wherein said analyzing includes applying a Generalized Likelihood Ratiotest with respect to said combination information.
 23. The method ofclaim 16, wherein said analyzing includes performing parallel filteringwith respect to said combination information.
 24. The method of claim15, wherein said adjusting includes adjusting said filter operation totemporarily increase responsiveness thereof to changes in the availablebandwidth.
 25. The method of claim 15, wherein said using includes usinga Kalman filter to perform said filter operation, and said selectivelyadjusting includes selectively adjusting a process noise covarianceparameter provided to the Kalman filter.